Enhanced Condition Monitoring Research Articles

Integration of Sensors for Enhanced Condition Monitoring in Polymer Gears: A Comparative Study of Acceleration and Temperature Sensors

As an integral part of a machine, gears are subject to wear, which is influenced by a number of factors. For polymer gears in particular, the uncertainties due to wear are high. These uncertainties outweigh the advantages of polymer gears, such as lower inertia. Improved condition monitoring, for example, with better data acquisition, could reduce these uncertainties and is therefore of great interest. This study addresses the challenges of condition monitoring in polymer gears by investigating the integration of sensors directly onto the gears for improved sensitivity. A compact sensor module mounted on a polymer gear is presented to demonstrate the benefits of integrated sensors. The research compares the effectiveness of integrated acceleration and temperature sensors with state of the art external methods. The results show that the in situ sensor module (ISM) provides reliable measurements for condition monitoring with integrated sensors. A comparative analysis with methods based on the current state of research highlights the increased sensitivity of condition monitoring based on the ISM acceleration sensors compared to traditional bearing block sensors. This increased sensitivity shows a clear advantage of integrated sensors over established methods. The temperature curve of the integrated sensors is sensitive to abrasive wear and gear failure, indicating the wider potential of integrated temperature sensors. In conclusion, this research lays the foundation for advanced condition monitoring using integrated sensors in polymer gears. The knowledge gained contributes to optimising gear applications, promoting cost-effectiveness and aligning with the principles of the Internet of Things and Industry 4.0.

Investigating wind turbine dynamic transient loads using contactless shaft torque measurements

The wind industry is showing increasing awareness about the importance of long-term direct shaft mechanical torque measurements to fully understand wind turbine (WT) dynamics, adopt proactive solutions for extreme load mitigation and enhance condition monitoring (CM) capabilities. Although torsional effects are important, torque measurement on such large, inaccessible machines is practically and logistically difficult, mainly because of the costly and intrusive specialised equipment currently available. This study details an experimental set-up for the investigation of shaft dynamic transient load and speed measurements through a contactless, low-cost torque meter. Results are obtained over a range of applied loads and compared with reference measurements from an in-line, invasive torque transducer. Average torque and speed root-mean-square error values of 0.53 Nm and 0.35 rpm, respectively, indicate good accuracy of the proposed contactless torque meter. Its implementation in the field would allow direct, cheap, real-time measurements of WT drive train loads for performance monitoring, control and CM purposes.

Review of Underground Storage Tank Condition Monitoring Techniques

This article aims to provide a comprehensive review on the condition monitoring techniques of underground storage tanks (UST). Generally, the UST has long been a favourite toxic substance reservation apparatus, thanks to its large capacity and minimum floor space requirement. Recently, attention has been drawn to the safety risks of the complex cylindrical-shaped system and its surrounding environment due to contamination resulting from unwanted subsurface leakage. Studies on related countermeasures shows that numerous efforts have been focused on the damage remediation process and fault detection practice; however, it has also been observed that there are uncertainties in present technical complications involving the effectiveness of corrective actions and the robustness of condition monitoring techniques. As an alternative means to deliver spatial information on structural integrity, the feasibility of integrating non- destructive evaluation (NDE) techniques with machine learning algorithms, on observing the degradation process of UST, so as to enhance condition monitoring competency, is discussed.

Enhanced lubricant management to reduce costs and minimise environmental impact

One of the important factors when selecting the more appropriate hydraulic fluid for use in applications is definitely its long service-life. By using hydraulic fluid with long service-life, together with enhanced condition monitoring of the fluid, we can significantly reduce costs and minimise environmental impact due to less lubricant production, logistics, and disposal.This paper describes a novel method for testing the durabilities of different hydraulic oils, followed later by addressing the condition-monitoring of hydraulic oils throughout their life-cycles. Particular emphasis is placed on quantitatively assessing, in detail, one mineral oil's condition and its remaining useful lifetime.This paper proposes a hybrid mathematical model based on gathered data from previously conducted oil-ageing tests. The presented model is able to successfully and accurately assess the oil's condition and its remaining useful lifetime after the oil has only reached one third of its lifetime.

Combining models of behaviour with operational data to provide enhanced condition monitoring of AGR cores

Installation of new monitoring equipment in Nuclear Power Plants (NPPs) is often difficult and expensive and therefore maximizing the information that can be extracted from existing monitoring equipment is highly desirable. This paper describes the process of combining models derived from laboratory experimentation with current operational plant data to infer an underlying measure of health. A demonstration of this process is provided where the fuel channel bore profile, a measure of core health, is inferred from data gathered during the refuelling process of an Advanced Gas-cooled Reactor (AGR) nuclear power plant core. Laboratory simulation was used to generate a model of an interaction between the fuel assembly and the core. This model is used to isolate a single frictional component from a noisy input signal and use this friction component as a measure of health to assess the current condition of the graphite bricks that comprise the core. In addition, the model is used to generate an expected refuelling response (the noisy input signal) for a given set of channel bore diameter measurements for either insertion of new fuel or removal of spent fuel, providing validation of the model. This benefit of this work is that it provides a greater understanding of the health of the graphite core, which is important for continued and extended operation of the AGR plants in the UK.

Extended PLS approach for enhanced condition monitoring of industrial processes

AbstractAn extended partial‐least squares (EPLS) algorithm is introduced to correct a deficiency of conventional partial least squares (PLS) when used as a tool to detect abnormal operating conditions in industrial processes. In the absence of feedback control, an abnormal operating condition that affects only process response variables will not be propagated back to the process predictor (or input) variables. Thus monitoring tools developed under the conventional PLS framework and based only on the predictor matrix will fail to detect the abnormal condition. The EPLS algorithm described removes this deficiency by defining new scores that are based on both predictor and response variables. The EPLS approach provides two monitoring charts to detect abnormal process behavior, as well as contribution charts to diagnose this behavior. To demonstrate the utility of the new approach, the extended algorithm and monitoring tools are applied to a realistic simulation of a fluid catalytic cracking unit and to a real industrial process that involves a complex chemical reaction.